The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Gambarajah Keputusan Perduaan (BDD) ialah struktur data penting untuk reka bentuk litar digital menggunakan alat VLSI CAD. Susunan pembolehubah mempengaruhi jumlah bilangan nod dan panjang laluan dalam BDD. Mencari susunan pembolehubah yang baik merupakan masalah pengoptimuman dan sebelum ini banyak pendekatan pengoptimuman telah dilaksanakan untuk BDD dalam beberapa kerja penyelidikan. Dalam makalah ini, pendekatan pengoptimuman berdasarkan algoritma Spider Monkey Optimization (SMO) dicadangkan untuk masalah pesanan pembolehubah BDD menyasarkan nombor nod dan panjang laluan terpanjang. SMO ialah pendekatan pengoptimuman berasaskan kecerdasan kawanan yang terkenal berdasarkan tingkah laku mencari makan monyet labah-labah. Kerja yang dicadangkan telah dibandingkan dengan pendekatan penyusunan semula BDD terkini yang lain menggunakan algoritma Pengoptimuman Particle Swarm (PSO). Keputusan yang diperoleh menunjukkan peningkatan yang ketara berbanding kaedah Pengoptimuman Particle Swarm. Kaedah berasaskan SMO yang dicadangkan digunakan pada litar digital penanda aras berbeza yang mempunyai tahap kerumitan yang berbeza. Kiraan nod dan panjang laluan terpanjang untuk bilangan maksimum litar yang diuji didapati lebih baik dalam SMO daripada PSO.
Mohammed BALAL SIDDIQUI
Jamia Millia Islamia
Mirza TARIQ BEG
Jamia Millia Islamia
Syed NASEEM AHMAD
Jamia Millia Islamia
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Salinan
Mohammed BALAL SIDDIQUI, Mirza TARIQ BEG, Syed NASEEM AHMAD, "Variable Ordering in Binary Decision Diagram Using Spider Monkey Optimization for Node and Path Length Optimization" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 7, pp. 976-989, July 2023, doi: 10.1587/transfun.2021EAP1108.
Abstract: Binary Decision Diagrams (BDDs) are an important data structure for the design of digital circuits using VLSI CAD tools. The ordering of variables affects the total number of nodes and path length in the BDDs. Finding a good variable ordering is an optimization problem and previously many optimization approaches have been implemented for BDDs in a number of research works. In this paper, an optimization approach based on Spider Monkey Optimization (SMO) algorithm is proposed for the BDD variable ordering problem targeting number of nodes and longest path length. SMO is a well-known swarm intelligence-based optimization approach based on spider monkeys foraging behavior. The proposed work has been compared with other latest BDD reordering approaches using Particle Swarm Optimization (PSO) algorithm. The results obtained show significant improvement over the Particle Swarm Optimization method. The proposed SMO-based method is applied to different benchmark digital circuits having different levels of complexities. The node count and longest path length for the maximum number of tested circuits are found to be better in SMO than PSO.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2021EAP1108/_p
Salinan
@ARTICLE{e106-a_7_976,
author={Mohammed BALAL SIDDIQUI, Mirza TARIQ BEG, Syed NASEEM AHMAD, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Variable Ordering in Binary Decision Diagram Using Spider Monkey Optimization for Node and Path Length Optimization},
year={2023},
volume={E106-A},
number={7},
pages={976-989},
abstract={Binary Decision Diagrams (BDDs) are an important data structure for the design of digital circuits using VLSI CAD tools. The ordering of variables affects the total number of nodes and path length in the BDDs. Finding a good variable ordering is an optimization problem and previously many optimization approaches have been implemented for BDDs in a number of research works. In this paper, an optimization approach based on Spider Monkey Optimization (SMO) algorithm is proposed for the BDD variable ordering problem targeting number of nodes and longest path length. SMO is a well-known swarm intelligence-based optimization approach based on spider monkeys foraging behavior. The proposed work has been compared with other latest BDD reordering approaches using Particle Swarm Optimization (PSO) algorithm. The results obtained show significant improvement over the Particle Swarm Optimization method. The proposed SMO-based method is applied to different benchmark digital circuits having different levels of complexities. The node count and longest path length for the maximum number of tested circuits are found to be better in SMO than PSO.},
keywords={},
doi={10.1587/transfun.2021EAP1108},
ISSN={1745-1337},
month={July},}
Salinan
TY - JOUR
TI - Variable Ordering in Binary Decision Diagram Using Spider Monkey Optimization for Node and Path Length Optimization
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 976
EP - 989
AU - Mohammed BALAL SIDDIQUI
AU - Mirza TARIQ BEG
AU - Syed NASEEM AHMAD
PY - 2023
DO - 10.1587/transfun.2021EAP1108
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2023
AB - Binary Decision Diagrams (BDDs) are an important data structure for the design of digital circuits using VLSI CAD tools. The ordering of variables affects the total number of nodes and path length in the BDDs. Finding a good variable ordering is an optimization problem and previously many optimization approaches have been implemented for BDDs in a number of research works. In this paper, an optimization approach based on Spider Monkey Optimization (SMO) algorithm is proposed for the BDD variable ordering problem targeting number of nodes and longest path length. SMO is a well-known swarm intelligence-based optimization approach based on spider monkeys foraging behavior. The proposed work has been compared with other latest BDD reordering approaches using Particle Swarm Optimization (PSO) algorithm. The results obtained show significant improvement over the Particle Swarm Optimization method. The proposed SMO-based method is applied to different benchmark digital circuits having different levels of complexities. The node count and longest path length for the maximum number of tested circuits are found to be better in SMO than PSO.
ER -